Non-codebook based precoding is a promising technology for a time division duplex (TDD) long term evolution-advanced (LTE-A) system, due to inherent channel reciprocity, namely symmetry between an uplink frequency and a downlink frequency. The industry widely accepts the assumption of uplink-downlink reciprocity and channel estimation is effectively performed based on this assumption.
In the case of non-codebook based precoding, the precoding matrix is obtained at the transmitting end. The transmitting end utilizes predicated channel status information (CSI) to calculate the precoding matrix. Common methods of calculating a precoding matrix include singular value decomposition (SVD), uniform channel decomposition (UCD) and the QR algorithm.
FIG. 1 is a structural diagram illustrating a transmitter and a receiver in a SVD-based multiple input multiple output (MIMO) system. Given that the transmitter of a base station 1 has N antennas and the receiver of a mobile terminal 2 has M antennas, the dimension of the effective uplink CSI, i.e., the spatial channel matrix, is M×N, and the spatial channel matrix may be denoted as HM×N. HM×N is processed according to SVD shown in formula (1):H=UDVH  (1)where U and V are the left singular vector matrix and the right singular vector matrix of H, respectively. Both U and V are a unitary matrix, i.e., it follows UUH=I=VVH, where I is an identity matrix and (•)H denotes hermitian operation resulting in a transposed complex conjugate. It follows that UεCN×N, i.e., the dimension of U is N×N, and that VεCM×M, i.e., the dimension of V is M×M. The rank r of the CSI matrix H satisfies r≦min(M,N). The diagonal matrix D can be represented as
      D    =                  [                                                            D                r                                                    0                                                          0                                      0                                      ]                    N        ×        M              ,where Dr=diag(λ1, λ2, . . . λr) and λi is singular value of H in descending order, i.e., λ1>λ2 > . . . λr.
The right singular vector matrix V obtained after SVD is a linear precoding matrix. Each column of V is called an eigenvector of HHH, which is related to the eigenmode of the communication channel. If rank self-adaption is required, column vectors corresponding to greater singular values are selected from the right singular vector matrix V to compose the precoding matrix.
Non-codebook based precoding requires a dedicated pilot, which means data symbols and pilot symbols are precoded together. Thus the receiving end can obtain the effective channel after precoding by only performing channel estimation, thereby facilitating data modulation.
Since accurate CSI can be obtained due to reciprocity between the uplink and the downlink in a TDD system, non-codebook based precoding can provide additional precoding gain. Generally and theoretically, the smaller a precoding granularity is, the higher the corresponding precoding gain becomes. Precoding granularity is defined as a unit to be precoded, such as one or more resource block (RB). FIG. 2 illustrates precoding performance corresponding to different precoding granularities in a single-layer beamforming case. As shown, given equal signal to noise ratio (SNR), the smaller the precoding granularity is utilized, the greater the system throughput becomes. A precoding granularity of 10 means that 10 RBs employ the same precoding matrix. However, there is difference between channel responses corresponding to the 10 RBs. Therefore, the greater a precoding granularity is set, the less accurately a precoding matrix weighting all the RBs in the precoding granularity matches the actual channel status of each RB in the precoded unit. Consequently, taking into account matching of a precoding matrix and a channel, it is desirable to have a smaller precoding granularity to obtain a greater precoding gain.
However, in a practical system, performance gain of precoding is affected by channel estimation error (3GPP R1-092794). Since a smaller precoding granularity utilizes a lower reference signal (RS) power, accuracy of channel estimation is reduced. Therefore, an appropriately selected precoding granularity inevitably affects the system capacity. Furthermore, selection of precoding granularity is also an important issue for a multiple-user multiple input multiple output (MU-MIMO) system or a coordinative multiple point (CoMP) system sensitive to different multi-path delays between different user equipments or cells.
The above conclusion relies on the fact that channel estimation can be performed only within a precoding granularity. This is because each precoding granularity corresponds to a different precoding matrix and a different precoding matrix will corrupt channel coherency among multiple precoded units. Therefore, taking precoding accuracy into account, it is desirable to have a smaller precoding granularity. On the other hand, taking channel estimation into account, it is desirable to have a greater precoding granularity. Consequently, these two factors inter-restrict.
In a prior art solution, a base station dynamically monitors the channel to obtain its real-time status and then selects a corresponding precoding granularity according to information such as channel coherency, signal to interference and noise ratio (SINR), etc. Then, the base station transmits the selected precoding granularity to a mobile terminal. The mobile terminal performs channel estimation according to this indication within the resource blocks limited by the precoding granularity. The terminal needs to be notified of such indication real-time. Therefore, a lot of time-frequency resources are occupied.